# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Basic word2vec example."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import math
import os
import random
from tempfile import gettempdir
import zipfile

import numpy as np
from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf

import json
import matplotlib.pyplot as plt

# Step 1: Download the data.
url = 'http://mattmahoney.net/dc/'

#首先将windwos中fonts目录下的simhei.ttf拷贝到/home/hadoop/.pyenv/versions/2.7.10/lib/python2.7/site-packages/matplotlib/mpl-data/fonts/ttf目录中，点击安装
#然后删除~/.cache/matplotlib的缓冲目录（这步非常重要）
#第三修改修改配置文件：
#[hadoop@p168 ~]$vim /home/hadoop/.pyenv/versions/2.7.10/lib/python2.7/site-packages/matplotlib/mpl-data/matplotlibrc
#文件路径参考1.c，根据实际情况修改，找到如下两项，去掉前面的#，并在font.sans-serif冒号后面加上SimHei，保持退出。
#font.family         : sans-serif        
#font.sans-serif     : SimHei, Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif     
#就是知道字库族为sans-serif,同时添加“SimHei”即宋体到字库族列表中，同时将找到

axes.unicode_minus，将True改为False，作用就是解决负号'-'显示为方块的问题
plt.rcParams[u'font.sans-serif']=['SimHei']    #用来正常显示中文标签
plt.rcParams['axes.unicode_minus']=False      #用来正常显示负号


# Read the data into a list of strings.
def read_data(filename): 
  
  with open(filename) as f:
    data = f.read()
  data=[word for word in data]
  return data

if __name__=='__main__':
  vocabulary=read_data('QuanSongCi.txt')
  print('Data size', len(vocabulary))

# Step 2: Build the dictionary and replace rare words with UNK token.
vocabulary_size = 5000


def build_dataset(words, n_words):
  """Process raw inputs into a dataset."""
  count = [['UNK', -1]]
  count.extend(collections.Counter(words).most_common(n_words - 1))
  dictionary = dict()
  for word, _ in count:
    dictionary[word] = len(dictionary)
  data = list()
  unk_count = 0
  for word in words:
    index = dictionary.get(word, 0)
    if index == 0:  # dictionary['UNK']
      unk_count += 1
    data.append(index)
  count[0][1] = unk_count
  reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
  return data, count, dictionary, reversed_dictionary

# Filling 4 global variables:
# data - list of codes (integers from 0 to vocabulary_size-1).
#   This is the original text but words are replaced by their codes
# count - map of words(strings) to count of occurrences
# dictionary - map of words(strings) to their codes(integers)
# reverse_dictionary - maps codes(integers) to words(strings)
if __name__=='__main__':
  data, count, dictionary, reverse_dictionary = build_dataset(vocabulary,vocabulary_size)
  
  with open('dictionary.json','w') as of:
  	json.dump(dictionary,of,ensure_ascii=False,indent=4)
  with open('reverse_dictionary.json','w') as of:
  	json.dump(reverse_dictionary,of,ensure_ascii=False,indent=4)
  	
  del vocabulary  # Hint to reduce memory.
  print('Most common words (+UNK)', count[:5])
  print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])

data_index = 0

# Step 3: Function to generate a training batch for the skip-gram model.
def generate_batch(batch_size, num_skips, skip_window):
  global data_index
  assert batch_size % num_skips == 0
  assert num_skips <= 2 * skip_window
  batch = np.ndarray(shape=(batch_size), dtype=np.int32)
  labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
  span = 2 * skip_window + 1  # [ skip_window target skip_window ]
  buffer = list()
  if data_index + span > len(data):
    data_index = 0
  buffer.extend(data[data_index:data_index + span])
  data_index += span
  for i in range(batch_size // num_skips):
    context_words = [w for w in range(span) if w != skip_window]
    words_to_use = random.sample(context_words, num_skips)
    for j, context_word in enumerate(words_to_use):
      batch[i * num_skips + j] = buffer[skip_window]
      labels[i * num_skips + j, 0] = buffer[context_word]
    if data_index == len(data):
      buffer[:] = data[:span]
      data_index = span
    else:
      buffer.append(data[data_index])
      data_index += 1
      if len(buffer)>span:
      	buffer = buffer[1:]
  # Backtrack a little bit to avoid skipping words in the end of a batch
  data_index = (data_index + len(data) - span) % len(data)
  return batch, labels

if __name__=='__main__':
  batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)
  for i in range(8):
  	print(batch[i], reverse_dictionary[batch[i]],'->', labels[i, 0], reverse_dictionary[labels[i, 0]])

# Step 4: Build and train a skip-gram model.

batch_size = 256
embedding_size = 128  # Dimension of the embedding vector.
skip_window = 3       # How many words to consider left and right.
num_skips = 2         # How many times to reuse an input to generate a label.
num_sampled = 64      # Number of negative examples to sample.

# We pick a random validation set to sample nearest neighbors. Here we limit the
# validation samples to the words that have a low numeric ID, which by
# construction are also the most frequent. These 3 variables are used only for
# displaying model accuracy, they don't affect calculation.
valid_size = 16     # Random set of words to evaluate similarity on.
valid_window = 100  # Only pick dev samples in the head of the distribution.
valid_examples = np.random.choice(valid_window, valid_size, replace=False)

if __name__=='__main__':
	graph = tf.Graph()

	with graph.as_default():
	  #Creates a variable to hold the global_step
	  global_step=tf.Variable(0,trainable=False,name='global_step',dtype=tf.int64)

	  # Input data.
	  train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
	  train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
	  valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

	  # Ops and variables pinned to the CPU because of missing GPU implementation
	  with tf.device('/gpu:0'):# /cpu:0
	    # Look up embeddings for inputs.
	    embeddings = tf.Variable(
		tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
	    embed = tf.nn.embedding_lookup(embeddings, train_inputs)

	    # Construct the variables for the NCE loss
	    nce_weights = tf.Variable(
		tf.truncated_normal([vocabulary_size, embedding_size],
		                    stddev=1.0 / math.sqrt(embedding_size)))
	    nce_biases = tf.Variable(tf.zeros([vocabulary_size]))

	  # Compute the average NCE loss for the batch.
	  # tf.nce_loss automatically draws a new sample of the negative labels each
	  # time we evaluate the loss.
	  # Explanation of the meaning of NCE loss:
	  #   http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
	  loss = tf.reduce_mean(
	      tf.nn.nce_loss(weights=nce_weights,
		             biases=nce_biases,
		             labels=train_labels,
		             inputs=embed,
		             num_sampled=num_sampled,
		             num_classes=vocabulary_size))

	  # Construct the SGD optimizer using a learning rate of 1.0.
	  optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss,global_step=global_step)

	  # Compute the cosine similarity between minibatch examples and all embeddings.
	  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
	  normalized_embeddings = embeddings / norm
	  valid_embeddings = tf.nn.embedding_lookup(
	      normalized_embeddings, valid_dataset)
	  similarity = tf.matmul(
	      valid_embeddings, normalized_embeddings, transpose_b=True)

	  # Add variable initializer.
	  init = tf.global_variables_initializer()

# Step 5: Begin training.
num_steps = 100001

if __name__=='__main__':
	with tf.Session(graph=graph) as session:
	  saver=tf.train.Saver(max_to_keep=5) ##cumtzd hw
	  # We must initialize all variables before we use them.
	  init.run()
	  print('Initialized')
	  
	  try:
	    checkpoint_path = tf.train.latest_checkpoint('./')
	    saver.restore(session,checkpoint_path)
	    print('restore from [{0}]'.format(checkpoint_path))
	  except Exception:
	    print('no check point found...')

	  average_loss = 0
	  for step in xrange(num_steps):
	    batch_inputs, batch_labels = generate_batch(
		batch_size, num_skips, skip_window)
	    feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}

	    # We perform one update step by evaluating the optimizer op (including it
	    # in the list of returned values for session.run()
	    gs, _, loss_val = session.run([global_step,optimizer, loss], feed_dict=feed_dict)
	    average_loss += loss_val

	    if gs % 5000 == 0:
	      if gs > 0:
                average_loss /= 5000
		# The average loss is an estimate of the loss over the last 2000 batches.
	      print('Average loss at step ', gs, ': ', average_loss)
	      average_loss = 0
	      
	      save_path = saver.save(session,os.path.join('./',"model.ckpt"),global_step=gs)

	    # Note that this is expensive (~20% slowdown if computed every 500 steps)
	    if gs % 20000 == 0:
	      sim = similarity.eval()
	      for i in xrange(valid_size):
                valid_word = reverse_dictionary[valid_examples[i]]
                top_k = 8  # number of nearest neighbors
                nearest = (-sim[i, :]).argsort()[1:top_k + 1]
                log_str = 'Nearest to %s:' % valid_word
                for k in xrange(top_k):
                  close_word = reverse_dictionary[nearest[k]]
                  log_str = '%s %s,' % (log_str, close_word)
                print(log_str)
                
	  final_embeddings = normalized_embeddings.eval()	  	  
	  np.save('embedding_{0}.npy'.format(gs),final_embeddings)          
	  save_path = saver.save(session,os.path.join('./',"model.ckpt"),global_step=gs)

# Step 6: Visualize the embeddings.


# pylint: disable=missing-docstring
# Function to draw visualization of distance between embeddings.
	def plot_with_labels(low_dim_embs, labels, filename):
	  assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'
	  plt.figure(figsize=(18, 18))  # in inches
	  for i, label in enumerate(labels):
	    x, y = low_dim_embs[i, :]
	    plt.scatter(x, y)
	    plt.annotate(label,
		         xy=(x, y),
		         xytext=(5, 2),
		         textcoords='offset points',
		         ha='right',
		         va='bottom')

	  plt.savefig(filename)

	try:
	  # pylint: disable=g-import-not-at-top
	  from sklearn.manifold import TSNE
	  import matplotlib.pyplot as plt

	  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')
	  plot_only = 500
	  low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
	  labels = [reverse_dictionary[i] for i in xrange(plot_only)]
	  plot_with_labels(low_dim_embs, labels, os.path.join('./', 'tsne.png'))

	except ImportError as ex:
	  print('Please install sklearn, matplotlib, and scipy to show embeddings.')
	  print(ex)
